Trajectory Tracking Control of an Intelligent Vehicle Based on T-S Fuzzy Variable Weight MPC

doi:10.3901/JME.2023.04.199

Abstract

Abstract: In order to coordinate the trajectory tracking accuracy and stability of intelligent driving vehicles and improve the self-adaptive capability of the control algorithm to different working conditions, a trajectory tracking control strategy based on T-S (Takagi-Sugeno) fuzzy variable weight model predictive control (MPC) is proposed. The MPC control is established with the front wheel steering angle as the control variable. The real-time lateral displacement deviation and yaw angle deviation are taken as the fuzzy inputs. The MPC objective function weights are optimized online by T-S fuzzy control to coordinate the influence of the weight matrix on the trajectory tracking accuracy and stability. A whole vehicle dynamic model of distributed drive electric vehicle is established based on Carsim, the control strategy is established based on Simulink, and the effectiveness of the proposed control strategy is verified through dynamics simulation real vehicle test on double-lane change working condition. Simulation results show that, compared with the traditional MPC control, the proposed T-S fuzzy variable weight MPC control can reduce the lateral displacement deviation by 62.24% and effectively improve the trajectory tracking accuracy. Moreover, it can reduce the front wheel steering angle fluctuation by 37.46% and the yaw angle deviation by 84.19%, which significantly enhances the trajectory tracking stability. The test results show that the lateral displacement deviation is within 0.12 m and the yaw angle deviation is within 1 ° when the vehicle makes double lane change at 20 km/h on asphalt pavement. Meanwhile, the curve of front wheel steering angle is smooth, which indicates that the proposed algorithm has good control effect and practicality.

Key words: intelligent driving, trajectory tracking, model prediction, T-S fuzzy, condition adaption

CLC Number:

U461

LI Shaohua, YANG Zekun, WANG Xuewei. Trajectory Tracking Control of an Intelligent Vehicle Based on T-S Fuzzy Variable Weight MPC[J]. Journal of Mechanical Engineering, 2023, 59(4): 199-212.

References

[1] ROKONUZZAMAN M, MOHAJER N, NAHAVANDI S,et al. Review and performance evaluation of path tracking controllers of autonomous vehicles[J]. IET Intelligent Transport Systems,2021,15(5):646-670.
[2] YAKUB F,MORI Y. Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2015,229(12):1695-1714.
[3] ATAEI M,KHAJEPOUR A,JEON S. Model predictive control for integrated lateral stability,traction/braking control,and rollover prevention of electric vehicles[J]. Vehicle System Dynamics,2019,58(1):49-73.
[4] JI J,KHAJEPOUR A,MELEK W,et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multi-constraints[J]. IEEE Transactions on Vehicular Technology,2017,66(2):952-964.
[5] GUO H,CAO D,ChEN H,et al. Model predictive path following control for autonomous cars considering a measurable disturbance:Implementation, testing,and verification[J]. Mechanical Systems and Signal Processing,2019,118:41-60.
[6] CUI Q,DING R,WEI C,et al. Path-tracking and lateral stabilisation for autonomous vehicles by using the steering angle envelope[J]. Vehicle System Dynamics,2021,59(11):1672-1696.
[7] CHENG S,LI L,CHEN X,et al. Model-Predictive-Control-Based path tracking controller of autonomous vehicle considering parametric uncertainties and velocity-varying[J]. IEEE Transactions on Industrial Electronics,2021,68(9):8698-8707.
[8] 王艺,蔡英凤,陈龙,等. 基于模型预测控制的智能网联汽车路径跟踪控制器设计[J]. 机械工程学报,2019,55(8):136-153. WANG Yi, CAI Yingfeng, CHEN Long,et al. Design of intelligent and connected vehicle path tracking controller based on model predictive control[J]. Journal of Mechanical Engineering,2019,55(8):136-153.
[9] 李绍松,郭孔辉,仇韬,等. 极限工况下主动前轮转向汽车稳定性控制[J]. 汽车工程,2020,42(2):191-198. LI Shaosong,GUO Konghui,QIU Tao,et al. Stability control of vehicle with active front steering under extreme conditions[J]. Automotive Engineering,2020,42(2):191-198.
[10] 梁艺潇,李以农,AMIR K,等. 基于转向与主动横摆力矩协调的四轮驱动智能电动汽车路径跟踪控制[J]. 机械工程学报,2021,57(6):142-155. LIANG Yixiao,LI Yinong,AMIR K,et al. Path following control for four-wheel drive electric intelligent vehicle based on coordination between steering and direct yaw moment system[J]. Journal of Mechanical Engineering,2021,57(6):142-155.
[11] AMIR M,GIVARGIS T. Hybrid state machine model for fast model predictive control:Application to path tracking[C]//IEEE/ACM International Conference on Computer-Aided Design,Digest of Technical Papers,ICCAD,2017:185-192.
[12] FUNKE J,BROWN M,ERLIEN S,et al. Collision avoidance and stabilization for autonomous vehicles in emergency scenarios[J]. IEEE Transactions on Control Systems Technology,2017,25(4):1204-1216.
[13] XUE W,ZHENG L. Active collision avoidance system design based on model predictive control with varying sampling time[J]. Automotive Innovation,2020,3(1):62-72.
[14] BAI G,MENG Y,LIU L,et al. A new path tracking method based on multilayer model predictive control[J]. Applied Sciences (Switzerland),2019,9(13):2649-2667.
[15] AMER N,ZAMZURI H,HUDHA K,et al. Modelling and control strategies in path tracking control for autonomous ground vehicles:A review of state of the art and challenges[J]. Journal of Intelligent and Robotic Systems:Theory and Applications,2017,86(2):225-254.
[16] 徐兴,汤赵,王峰,等. 基于变权重系数的分布式驱动无人车轨迹跟踪[J]. 中国公路学报,2019,32(12):36-45. XU Xing,TANG Zhao,WANG Feng,et al. Varied weight coefficients based trajectory tracking of distributed drve self-driving vehicle[J]. China Journal of Highway and Transport,2019,32(12):36-45.
[17] WANG J, TENG F, LI J, et al. Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment[J]. Advances in Mechanical Engineering,2021,13(3):1-16.